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1. A Three Steps Methodological Approach to Legal Governance Validation

Author

Casanovas, Pompeu, Hashmi, Mustafa, de Koker, Louis, and Lam, Ho-Pun

Subjects
Computer Science - Computers and Society, C.5.0, I.2.4, J.4, K.4.2, K.4.1, K.5.2
Abstract

We present in this position paper a methodology to validate legal governance regulatory models from an empirical approach, as illustrated by means of three diagrams: (i) a scheme drawing the rule and meta-rule of law; (ii) a metamodel for legal governance; (iii) a causal validation scheme for legal compliance. These visualisations refer to different sets of notions corresponding respectively to (i) a general scheme with three dimensions and four clusters, (ii) a meta-model encompassing legal compliance through design (LCtD) and ecological validity, and (iii) the con-struction of an empirical validation model of causal chains. The final aim of the methodology is to build and test smart legal ecosystems (SLE) for Industry 4.0 and 5.0., Comment: Preprint. To be published in: Palmirani, M. et al. (2024). AI Approaches to the Complexity of Legal Systems. Reviewed, selected papers. AICOL XIII-XIV, LNAI, Cham: Springer, 2024

Published
2024

3. UNITS: A Unified Multi-Task Time Series Model

Author

Gao, Shanghua, Koker, Teddy, Queen, Owen, Hartvigsen, Thomas, Tsiligkaridis, Theodoros, and Zitnik, Marinka

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Advances in time series models are driving a shift from conventional deep learning methods to pre-trained foundational models. While pre-trained transformers and reprogrammed text-based LLMs report state-of-the-art results, the best-performing architectures vary significantly across tasks, and models often have limited scope, such as focusing only on time series forecasting. Models that unify predictive and generative time series tasks under a single framework remain challenging to achieve. We introduce UniTS, a multi-task time series model that uses task tokenization to express predictive and generative tasks within a single model. UniTS leverages a modified transformer block designed to obtain universal time series representations. This design induces transferability from a heterogeneous, multi-domain pre-training dataset-often with diverse dynamic patterns, sampling rates, and temporal scales-to many downstream datasets, which can also be diverse in task specifications and data domains. Across 38 datasets spanning human activity sensors, healthcare, engineering, and finance domains, UniTS model performs favorably against 12 forecasting models, 20 classification models, 18 anomaly detection models, and 16 imputation models, including repurposed text-based LLMs. UniTS demonstrates effective few-shot and prompt learning capabilities when evaluated on new data domains and tasks. In the conventional single-task setting, UniTS outperforms strong task-specialized time series models. The source code and datasets are available at https://github.com/mims-harvard/UniTS.

Published
2024

4. Higher-Order Equivariant Neural Networks for Charge Density Prediction in Materials

Author

Koker, Teddy, Quigley, Keegan, Taw, Eric, Tibbetts, Kevin, and Li, Lin

Subjects
Physics - Computational Physics, Condensed Matter - Materials Science, Computer Science - Machine Learning
Abstract

The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge. We introduce ChargE3Net, an E(3)-equivariant graph neural network for predicting electron density in atomic systems. ChargE3Net enables the learning of higher-order equivariant feature to achieve high predictive accuracy and model expressivity. We show that ChargE3Net exceeds the performance of prior work on diverse sets of molecules and materials. When trained on the massive dataset of over 100K materials in the Materials Project database, our model is able to capture the complexity and variability in the data, leading to a significant 26.7% reduction in self-consistent iterations when used to initialize DFT calculations on unseen materials. Furthermore, we show that non-self-consistent DFT calculations using our predicted charge densities yield near-DFT performance on electronic and thermodynamic property prediction at a fraction of the computational cost. Further analysis attributes the greater predictive accuracy to improved modeling of systems with high angular variations. These results illuminate a pathway towards a machine learning-accelerated ab initio calculations for materials discovery.

Published
2023

6. Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency

Author

Queen, Owen, Hartvigsen, Thomas, Koker, Teddy, He, Huan, Tsiligkaridis, Theodoros, and Zitnik, Marinka

Subjects
Computer Science - Machine Learning
Abstract

Interpreting time series models is uniquely challenging because it requires identifying both the location of time series signals that drive model predictions and their matching to an interpretable temporal pattern. While explainers from other modalities can be applied to time series, their inductive biases do not transfer well to the inherently challenging interpretation of time series. We present TimeX, a time series consistency model for training explainers. TimeX trains an interpretable surrogate to mimic the behavior of a pretrained time series model. It addresses the issue of model faithfulness by introducing model behavior consistency, a novel formulation that preserves relations in the latent space induced by the pretrained model with relations in the latent space induced by TimeX. TimeX provides discrete attribution maps and, unlike existing interpretability methods, it learns a latent space of explanations that can be used in various ways, such as to provide landmarks to visually aggregate similar explanations and easily recognize temporal patterns. We evaluate TimeX on eight synthetic and real-world datasets and compare its performance against state-of-the-art interpretability methods. We also conduct case studies using physiological time series. Quantitative evaluations demonstrate that TimeX achieves the highest or second-highest performance in every metric compared to baselines across all datasets. Through case studies, we show that the novel components of TimeX show potential for training faithful, interpretable models that capture the behavior of pretrained time series models., Comment: Accepted to NeurIPS 2023 (spotlight)

Published
2023

7. Towards a Global Approach to Combating Financial Crime

Author

de Koker, Louis, Goldbarsht, Doron, Sellers, Mortimer, Series Editor, Maxeiner, James, Series Editor, Antonovych, Myroslava, Editorial Board Member, de Araújo, Nadia, Editorial Board Member, Bakšic-Muftic, Jasna, Editorial Board Member, Carey Miller, David L., Editorial Board Member, Musse Félix, Loussia P., Editorial Board Member, Gross, Emanuel, Editorial Board Member, Hickey Jr., James E., Editorial Board Member, Klabbers, Jan, Editorial Board Member, Marques, Cláudia Lima, Editorial Board Member, Masferrer, Aniceto, Editorial Board Member, Millard, Eric, Editorial Board Member, Moens, Gabriël A., Editorial Board Member, Pangalangan, Raul C., Editorial Board Member, Pinto, Ricardo Leite, Editorial Board Member, Rahman, Mizanur, Editorial Board Member, Sato, Keita, Editorial Board Member, Saxena, Poonam, Editorial Board Member, Simpson, Gerry, Editorial Board Member, Somers, Eduard, Editorial Board Member, Sun, Xinqiang, Editorial Board Member, Tomaszewski, Tadeusz, Editorial Board Member, de Zwaan, Jaap, Editorial Board Member, Goldbarsht, Doron, editor, and de Koker, Louis, editor

Published
2024
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8. Global Standard-Setting on Financial Crime: Navigating Challenges

Author

Goldbarsht, Doron, de Koker, Louis, Sellers, Mortimer, Series Editor, Maxeiner, James, Series Editor, Antonovych, Myroslava, Editorial Board Member, de Araújo, Nadia, Editorial Board Member, Bakšic-Muftic, Jasna, Editorial Board Member, Carey Miller, David L., Editorial Board Member, Musse Félix, Loussia P., Editorial Board Member, Gross, Emanuel, Editorial Board Member, Hickey Jr., James E., Editorial Board Member, Klabbers, Jan, Editorial Board Member, Marques, Cláudia Lima, Editorial Board Member, Masferrer, Aniceto, Editorial Board Member, Millard, Eric, Editorial Board Member, Moens, Gabriël A., Editorial Board Member, Pangalangan, Raul C., Editorial Board Member, Pinto, Ricardo Leite, Editorial Board Member, Rahman, Mizanur, Editorial Board Member, Sato, Keita, Editorial Board Member, Saxena, Poonam, Editorial Board Member, Simpson, Gerry, Editorial Board Member, Somers, Eduard, Editorial Board Member, Sun, Xinqiang, Editorial Board Member, Tomaszewski, Tadeusz, Editorial Board Member, de Zwaan, Jaap, Editorial Board Member, Goldbarsht, Doron, editor, and de Koker, Louis, editor

Published
2024
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9. ‘De-Risking’, De-Banking and Denials of Bank Services: An Over-Compliance Dilemma?

Author

de Koker, Louis, Casanovas, Pompeu, Sellers, Mortimer, Series Editor, Maxeiner, James, Series Editor, Antonovych, Myroslava, Editorial Board Member, de Araújo, Nadia, Editorial Board Member, Bakšic-Muftic, Jasna, Editorial Board Member, Carey Miller, David L., Editorial Board Member, Musse Félix, Loussia P., Editorial Board Member, Gross, Emanuel, Editorial Board Member, Hickey Jr., James E., Editorial Board Member, Klabbers, Jan, Editorial Board Member, Marques, Cláudia Lima, Editorial Board Member, Masferrer, Aniceto, Editorial Board Member, Millard, Eric, Editorial Board Member, Moens, Gabriël A., Editorial Board Member, Pangalangan, Raul C., Editorial Board Member, Pinto, Ricardo Leite, Editorial Board Member, Rahman, Mizanur, Editorial Board Member, Sato, Keita, Editorial Board Member, Saxena, Poonam, Editorial Board Member, Simpson, Gerry, Editorial Board Member, Somers, Eduard, Editorial Board Member, Sun, Xinqiang, Editorial Board Member, Tomaszewski, Tadeusz, Editorial Board Member, de Zwaan, Jaap, Editorial Board Member, Goldbarsht, Doron, editor, and de Koker, Louis, editor

Published
2024
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10. The FATF’s Combating of Financing of Proliferation Standards: Private Sector Implementation Challenges

Author

de Koker, Louis, Sellers, Mortimer, Series Editor, Maxeiner, James, Series Editor, Antonovych, Myroslava, Editorial Board Member, de Araújo, Nadia, Editorial Board Member, Bakšic-Muftic, Jasna, Editorial Board Member, Carey Miller, David L., Editorial Board Member, Musse Félix, Loussia P., Editorial Board Member, Gross, Emanuel, Editorial Board Member, Hickey Jr., James E., Editorial Board Member, Klabbers, Jan, Editorial Board Member, Marques, Cláudia Lima, Editorial Board Member, Masferrer, Aniceto, Editorial Board Member, Millard, Eric, Editorial Board Member, Moens, Gabriël A., Editorial Board Member, Pangalangan, Raul C., Editorial Board Member, Pinto, Ricardo Leite, Editorial Board Member, Rahman, Mizanur, Editorial Board Member, Sato, Keita, Editorial Board Member, Saxena, Poonam, Editorial Board Member, Simpson, Gerry, Editorial Board Member, Somers, Eduard, Editorial Board Member, Sun, Xinqiang, Editorial Board Member, Tomaszewski, Tadeusz, Editorial Board Member, de Zwaan, Jaap, Editorial Board Member, Goldbarsht, Doron, editor, and de Koker, Louis, editor

Published
2024
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11. FATF’s Risk-Based Approach: Has the Pendulum Swung too Far?

Author

de Koker, Louis, Goldbarsht, Doron, Sellers, Mortimer, Series Editor, Maxeiner, James, Series Editor, Antonovych, Myroslava, Editorial Board Member, de Araújo, Nadia, Editorial Board Member, Bakšic-Muftic, Jasna, Editorial Board Member, Carey Miller, David L., Editorial Board Member, Musse Félix, Loussia P., Editorial Board Member, Gross, Emanuel, Editorial Board Member, Hickey Jr., James E., Editorial Board Member, Klabbers, Jan, Editorial Board Member, Marques, Cláudia Lima, Editorial Board Member, Masferrer, Aniceto, Editorial Board Member, Millard, Eric, Editorial Board Member, Moens, Gabriël A., Editorial Board Member, Pangalangan, Raul C., Editorial Board Member, Pinto, Ricardo Leite, Editorial Board Member, Rahman, Mizanur, Editorial Board Member, Sato, Keita, Editorial Board Member, Saxena, Poonam, Editorial Board Member, Simpson, Gerry, Editorial Board Member, Somers, Eduard, Editorial Board Member, Sun, Xinqiang, Editorial Board Member, Tomaszewski, Tadeusz, Editorial Board Member, de Zwaan, Jaap, Editorial Board Member, Goldbarsht, Doron, editor, and de Koker, Louis, editor

Published
2024
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12. Financial Crime and the Law: Identifying and Mitigating Risks

Author

Goldbarsht, Doron, de Koker, Louis, Sellers, Mortimer, Series Editor, Maxeiner, James, Series Editor, Antonovych, Myroslava, Editorial Board Member, de Araújo, Nadia, Editorial Board Member, Bakšic-Muftic, Jasna, Editorial Board Member, Carey Miller, David L., Editorial Board Member, Musse Félix, Loussia P., Editorial Board Member, Gross, Emanuel, Editorial Board Member, Hickey Jr., James E., Editorial Board Member, Klabbers, Jan, Editorial Board Member, Marques, Cláudia Lima, Editorial Board Member, Masferrer, Aniceto, Editorial Board Member, Millard, Eric, Editorial Board Member, Moens, Gabriël A., Editorial Board Member, Pangalangan, Raul C., Editorial Board Member, Pinto, Ricardo Leite, Editorial Board Member, Rahman, Mizanur, Editorial Board Member, Sato, Keita, Editorial Board Member, Saxena, Poonam, Editorial Board Member, Simpson, Gerry, Editorial Board Member, Somers, Eduard, Editorial Board Member, Sun, Xinqiang, Editorial Board Member, Tomaszewski, Tadeusz, Editorial Board Member, de Zwaan, Jaap, Editorial Board Member, Goldbarsht, Doron, editor, and de Koker, Louis, editor

Published
2024
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16. Higher-order equivariant neural networks for charge density prediction in materials

Author

Teddy Koker, Keegan Quigley, Eric Taw, Kevin Tibbetts, and Lin Li

Subjects
Materials of engineering and construction. Mechanics of materials, TA401-492, Computer software, QA76.75-76.765
Abstract

Abstract The calculation of electron density distribution using density functional theory (DFT) in materials and molecules is central to the study of their quantum and macro-scale properties, yet accurate and efficient calculation remains a long-standing challenge. We introduce ChargE3Net, an E(3)-equivariant graph neural network for predicting electron density in atomic systems. ChargE3Net enables the learning of higher-order equivariant features to achieve high predictive accuracy and model expressivity. We show that ChargE3Net exceeds the performance of prior work on diverse sets of molecules and materials. When trained on the massive dataset of over 100K materials in the Materials Project database, our model is able to capture the complexity and variability in the data, leading to a significant 26.7% reduction in self-consistent iterations when used to initialize DFT calculations on unseen materials. Furthermore, we show that non-self-consistent DFT calculations using our predicted charge densities yield near-DFT performance on electronic and thermodynamic property prediction at a fraction of the computational cost. Further analysis attributes the greater predictive accuracy to improved modeling of systems with high angular variations. These results illuminate a pathway towards a machine learning-accelerated ab initio calculations for materials discovery.

Published
2024
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17. Domain Adaptation for Time Series Under Feature and Label Shifts

Author

He, Huan, Queen, Owen, Koker, Teddy, Cuevas, Consuelo, Tsiligkaridis, Theodoros, and Zitnik, Marinka

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Unsupervised domain adaptation (UDA) enables the transfer of models trained on source domains to unlabeled target domains. However, transferring complex time series models presents challenges due to the dynamic temporal structure variations across domains. This leads to feature shifts in the time and frequency representations. Additionally, the label distributions of tasks in the source and target domains can differ significantly, posing difficulties in addressing label shifts and recognizing labels unique to the target domain. Effectively transferring complex time series models remains a formidable problem. We present Raincoat, the first model for both closed-set and universal domain adaptation on complex time series. Raincoat addresses feature and label shifts by considering both temporal and frequency features, aligning them across domains, and correcting for misalignments to facilitate the detection of private labels. Additionally, Raincoat improves transferability by identifying label shifts in target domains. Our experiments with 5 datasets and 13 state-of-the-art UDA methods demonstrate that Raincoat can improve transfer learning performance by up to 16.33% and can handle both closed-set and universal domain adaptation., Comment: Accepted by ICML 2023; 29 pages (14 pages main paper + 15 pages supplementary materials). Code: see https://github.com/mims-harvard/Raincoat

Published
2023

20. Tail Length for Estimating Traffic Load Effects

Author

Bakker, Frederik P., Lenner, Roman, de Koker, Nico, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Matos, José C., editor, Lourenço, Paulo B., editor, Oliveira, Daniel V., editor, Branco, Jorge, editor, Proske, Dirk, editor, Silva, Rui A., editor, and Sousa, Hélder S., editor

Published
2024
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21. Graph Contrastive Learning for Materials

Author

Koker, Teddy, Quigley, Keegan, Spaeth, Will, Frey, Nathan C., and Li, Lin

Subjects
Computer Science - Machine Learning, Condensed Matter - Materials Science
Abstract

Recent work has shown the potential of graph neural networks to efficiently predict material properties, enabling high-throughput screening of materials. Training these models, however, often requires large quantities of labelled data, obtained via costly methods such as ab initio calculations or experimental evaluation. By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks. With the addition of a novel loss function, our framework is able to learn representations competitive with engineered fingerprinting methods. We also demonstrate that via model finetuning, contrastive pretraining can improve the performance of graph neural networks for prediction of material properties and significantly outperform traditional ML models that use engineered fingerprints. Lastly, we observe that CrystalCLR produces material representations that form clusters by compound class., Comment: 7 pages, 3 figures, NeurIPS 2022 AI for Accelerated Materials Design Workshop

Published
2022

23. Early adverse physiological event detection using commercial wearables: challenges and opportunities

Author

Jesse Phipps, Bryant Passage, Kaan Sel, Jonathan Martinez, Milad Saadat, Teddy Koker, Natalie Damaso, Shakti Davis, Jeffrey Palmer, Kajal Claypool, Christopher Kiley, Roderic I. Pettigrew, and Roozbeh Jafari

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Data from commercial off-the-shelf (COTS) wearables leveraged with machine learning algorithms provide an unprecedented potential for the early detection of adverse physiological events. However, several challenges inhibit this potential, including (1) heterogeneity among and within participants that make scaling detection algorithms to a general population less precise, (2) confounders that lead to incorrect assumptions regarding a participant’s healthy state, (3) noise in the data at the sensor level that limits the sensitivity of detection algorithms, and (4) imprecision in self-reported labels that misrepresent the true data values associated with a given physiological event. The goal of this study was two-fold: (1) to characterize the performance of such algorithms in the presence of these challenges and provide insights to researchers on limitations and opportunities, and (2) to subsequently devise algorithms to address each challenge and offer insights on future opportunities for advancement. Our proposed algorithms include techniques that build on determining suitable baselines for each participant to capture important physiological changes and label correction techniques as it pertains to participant-reported identifiers. Our work is validated on potentially one of the largest datasets available, obtained with 8000+ participants and 1.3+ million hours of wearable data captured from Oura smart rings. Leveraging this extensive dataset, we achieve pre-symptomatic detection of COVID-19 with a performance receiver operator characteristic (ROC) area under the curve (AUC) of 0.725 without correction techniques, 0.739 with baseline correction, 0.740 with baseline correction and label correction on the training set, and 0.777 with baseline correction and label correction on both the training and the test set. Using the same respective paradigms, we achieve ROC AUCs of 0.919, 0.938, 0.943 and 0.994 for the detection of self-reported fever, and 0.574, 0.611, 0.601, and 0.635 for detection of self-reported shortness of breath. These techniques offer improvements across almost all metrics and events, including PR AUC, sensitivity at 75% specificity, and precision at 75% recall. The ring allows continuous monitoring for detection of event onset, and we further demonstrate an improvement in the early detection of COVID-19 from an average of 3.5 days to an average of 4.1 days before a reported positive test result.

Published
2024
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24. Nominal ranking technique in information and knowledge management: A methodology to SoTL

Author

Lucian T. De Koker and Tanya Du Plessis

Subjects
scholarship of teaching and learning, sotl, nominal ranking technique, heat maps, information and knowledge management, business case studies, Management information systems, T58.6-58.62, Information theory, Q350-390
Abstract

Background: The scholarship of teaching and learning (SoTL) makes provision for methodology contributions across disciplines. The information and knowledge management (IKM) discipline prepares students to function optimally in the Fourth Industrial Revolution (4IR). To keep abreast with continuous and agile change in the 4IR, a new contribution is made to the SoTL in the form of a nominal ranking technique (NRT) methodology in the IKM discipline. Objectives: To develop the NRT methodology for constructing IKM heat maps of the 4IR elements and criteria relating to strategic information management (SIM) business case studies (BCSs). By sharing the NRT methodology, this paper aims to make a SoTL contribution in IKM. Method: Using the qualitative research approach, content analysis was used to analyse 101 BCSs of SIM students. By employing critical case sampling and the NRT methodology, the top BCSs were identified for inclusion in constructing the IKM heat maps. Results: Four BCS categories, unique, novice, duplicate, and duplicate-novice, were determined by applying the NRT methodology, with 5 elements and 25 criteria in constructing the IKM heat maps. Conclusion: The NRT methodology serves as a guideline for constructing IKM heat maps, a unique contribution to the SoTL in the IKM discipline. Each application of the NRT methodology will be unique and beneficial. Contribution: In this study, the value relates how the IKM discipline prepares SIM students to function optimally in the 4IR, evidenced by the four categories of BCSs.

Published
2024
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25. International Nosocomial Infection Control Consortium (INICC) report of health care associated infections, data summary of 45 countries for 2015 to 2020, adult and pediatric units, device-associated module

Author

Rosenthal, Victor D., Yin, Ruijie, Nercelles, Patricio, Rivera-Molina, Sara E., Jyoti, Somani, Dongol, Reshma, Aguilar-De-Moros, Daisy, Tumu, Nellie, Alarcon-Rua, Johana, Stagnaro, Juan P., Alkhawaja, Safaa, Jimenez-Alvarez, Luisa F., Cano-Medina, Yuliana A., Valderrama-Beltran, Sandra L., Henao-Rodas, Claudia M., Zuniga-Chavarria, Maria A., El-Kholy, Amani, Agha, Hala Mounir, Sahu, Suneeta, Anusandhan, Siksha O., Bhattacharyya, Mahuya, Kharbanda, Mohit, Poojary, Aruna, Nair, Pravin K., Myatra, Sheila N., Chawla, Rajesh, Sandhu, Kavita, Mehta, Yatin, Rajhans, Prasad, Zand, Farid, Abdellatif-Daboor, Mohammad, Tai, Chian-Wern, Gan, Chin S., Mat Nor, Mohd-Basri, Aguirre-Avalos, Guadalupe, Hernandez-Chena, Blanca E., Sassoe-Gonzalez, Alejandro, Villegas-Mota, Isabel, Aleman- Bocanegra, Mary C., Bat-Erdene, Ider, Carreazo, Nilton Y., Castaneda-Sabogal, Alex, Janc, Jarosław, Belskiy, Vladislav, Hlinkova, Sona, Yildizdas, Dincer, Havan, Merve, Koker, Alper, Sungurtekin, Hulya, Dinleyici, Ener C., Guclu, Ertugrul, Tao, Lili, Memish, Ziad A., and Jin, Zhilin

Published
2024
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28. The Conceptual Nuances of Technology-Supported Learning in Engineering

Author

Wolff, Karin, Kruger, Karel, Pott, Robert, and de Koker, Nico

Abstract

Enabling theory-practice bridging in engineering education is essential for developing twenty-first century graduate capabilities. Massification, resource constraints, and technological development have resulted in significant shifts to alternative forms of practical engagement, such as the use of online laboratories, but how do these contribute to learning? Based on three illustrative case studies at a research-intensive institution in the Global South, this paper offers a conceptualisation of the degrees of complexity entailed in multimodal approaches to teaching Fluid Mechanics, Finite Element Analysis and Control Systems at different stages of their respective programmes The paper examines the different levels of abstract-concrete learning when students engage with verbal, symbolic, graphic and physical representational artefacts designed to enable cumulative learning. The conceptual instruments are theoretically and methodologically drawn from Legitimation Code Theory dimensions, which lend themselves to the graphic analysis of knowledge practices. It is suggested that the explicit integration of and shifting between levels of abstraction and complexity with different kinds of technologies enables the kind of cumulative learning necessary to prepare technically-equipped graduates for complex twenty-first century engineering contexts.

Published
2023
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31. Early trajectories of virological and immunological biomarkers and clinical outcomes in patients admitted to hospital for COVID-19: an international, prospective cohort study

Author

Sahner, David, Tierney, John, Vogel, Susan E., Herpin, Betsey R., Smolskis, Mary C., McKay, Laura A., Cahill, Kelly, Crew, Page, Sardana, Ratna, Raim, Sharon Segal, Hensely, Lisa, Lorenzo, Johsua, Mock, Rebecca, Zuckerman, Judith, Atri, Negin, Miller, Mark, Vallee, David, Chung, Lucy, Kang, Nayon, Barrett, Kevin, Adam, Stacey J., Read, Sarah, Draghia-Akli, Ruxandra, Currier, Judy, Hughes, Eric, Harrigan, Rachel H., Amos, Laura, Carlsen, Amy, Carter, Anita, Collins, Gary, Davis, Bionca, Denning, Eileen, DuChene, Alain, Eckroth, Kate, Engen, Nicole, Frase, Alex, Gandits, Greg, Grund, Birgit, Harrison, Merrie, Hurlbut, Nancy, Kaiser, Payton, Koopmeiners, Joseph, Larson, Gregg, Meger, Sue, Mistry, Shweta Sharma, Murray, Thomas, Nelson, Ray, Quan, Kien, Quan, Siu Fun, Reilly, Cavan, Siegel, Lianne, Thompson, Greg, Vock, David, Walski, Jamie, Gelijns, Annetine C., Moskowitz, Alan J., Bagiella, Emilia, Moquete, Ellen, O'Sullivan, Karen, Marks, Mary E., Accardi, Evan, Kinzel, Emily, Burris, Sarah, Bedoya, Gabriela, Gupta, Lola, Overbey, Jessica R., Santos, Milerva, Gillinov, Marc A., Miller, Marissa A., Taddei-Peters, Wendy C., Fenton, Kathleen, Sandkovsky, Uriel, Gottlieb, Robert L., Mack, Michael, Berhe, Mezgebe, Haley, Clinton, Dishner, Emma, Bettacchi, Christopher, Golden, Kevin, Duhaime, Erin, Ryan, Madison, Tallmadge, Catherine, Estrada, Lorie, Jones, Felecia, Villa, Samatha, Wang, Samatha, Robert, Raven, Coleman, Tanquinisha, Clariday, Laura, Baker, Rebecca, Hurutado-Rodriguez, Mariana, Iram, Nazia, Fresnedo, Michelle, Davis, Allyson, Leonard, Kiara, Ramierez, Noelia, Thammavong, Jon, Duque, Krizia, Turner, Emma, Fisher, Tammy, Robinson, Dianna, Ransom, Desirae, Maldonado, Nicholas, Lusk, Erica, Killian, Aaron, Palacious, Adriana, Solis, Edilia, Jerrow, Janet, Watts, Matthew, Whitacre, Heather, Cothran, Elizabeth, Smith, Peter K., Barkauskas, Christina E., Vekstein, Andrew M., Ko, Emily R., Dreyer, Grace R., Stafford, Neil, Brooks, Megan, Der, Tatyana, Witte, Marie, Gamarallage, Ruwan, Franzone, John, Ivey, Noel, Lumsden, Rebecca H., Mosaly, Nilima, Mourad, Ahmaad, Holland, Thomas L., Motta, Mary, Lane, Kathleen, McGowan, Lauren M., Stout, Jennifer, Aloor, Heather, Bragg, Kennesha M., Toledo, Barvina, McLendon-Arvik, Beth, Bussadori, Barbara, Hollister, Beth A., Griffin, Michelle, Giangiacomo, Dana M., Rodriguez, Vicente, Bokhart, Gordon, Eichman, Sharon M., Parrino, Patrick E., Spindel, Stephen, Bansal, Aditya, Baumgarten, Katherine, Hand, Johnathan, Vonderhaar, Derek, Nossaman, Bobby, Sylvia Laudun, Ames, DeAnna, Broussard, Shane, Hernandez, Nilmo, Isaac, Geralyn, Dinh, Huan, Zheng, Yiling, Tran, Sonny, McDaniel, Hunter, Crovetto, Nicolle, Perin, Emerson, Costello, Briana, Manian, Prasad, Sohail, M. Rizwan, Postalian, Alexander, Hinsu, Punit, Watson, Carolyn, Chen, James, Fink, Melyssa, Sturgis, Lydia, Walker, Kim, Mahon, Kim, Parenti, Jennifer, Kappenman, Casey, Knight, Aryn, Sturek, Jeffrey M., Barros, Andrew, Enfield, Kyle B., Kadl, Alexandra, Green, China J., Simon, Rachel M., Fox, Ashley, Thornton, Kara, Adams, Amy, Badhwar, Vinay, Sharma, Sunil, Peppers, Briana, McCarthy, Paul, Krupica, Troy, Sarwari, Arif, Reece, Rebecca, Fornaresico, Lisa, Glaze, Chad, Evans, Raquel, Di, Fang, Carlson, Shawn, Aucremanne, Tanja, Tennant, Connie, Sutton, Lisa Giblin, Buterbaugh, Sabrina, Williams, Roger, Bunner, Robin, Traverse, Jay H., Rhame, Frank, Huelster, Joshua, Kethireddy, Rajesh, Davies, Irena, Salamanca, Julianne, Majeski, Christine, Skelton, Paige, Zarambo, Maria, Sarafolean, Andrea, Bowdish, Michael E., Borok, Zea, Wald-Dickler, Noah, Hutcheon, Douglass, Towfighi, Amytis, Lee, Mary, Lewis, Meghan R., Spellberg, Brad, Sher, Linda, Sharma, Aniket, Olds, Anna P., Justino, Chris, Loxano, Edward, Romero, Chris, Leong, Janet, Rodina, Valentina, Quesada, Christine, Hamilton, Luke, Escobar, Jose, Leshnower, Brad, Bender, William, Sharifpour, Milad, Miller, Jeffrey, Farrington, Woodrow, Baio, Kim T., McBride, Mary, Fielding, Michele, Mathewson, Sonya, Porte, Kristina, Maton, Missy, Ponder, Chari, Haley, Elisabeth, Spainhour, Christine, Rogers, Susan, Tyler, Derrick, Madathil, Ronson J., Rabin, Joseph, Levine, Andrea, Saharia, Kapil, Tabatabai, Ali, Lau, Christine, Gammie, James S., Peguero, Maya-Loren, McKernan, Kimberly, Audette, Mathew, Fleischmann, Emily, Akbari, Kreshta, Lee, Myounghee, Chi, Andrew, Salehi, Hanna, Pariser, Alan, Nyguyen, Phuong Tran, Moore, Jessica, Gee, Adrienne, Vincent, Shelika, Zuckerman, Richard A., Iribarne, Alexander, Metzler, Sara, Shipman, Samantha, Johnson, Haley, Newton, Crystallee, Parr, Doug, Miller, Leslie, Schelle, Beth, McLean, Sherry, Rothbaum, Howard R., Alvarez, Michael S., Kalan, Shivam P., Germann, Heather H., Hendershot, Jennifer, Moroney, Karen, Herring, Karen, Cook, Sharri, Paul, Pam, Walker-Ignasiak, Rebecca, North, Crystal, Oldmixon, Cathryn, Ringwood, Nancy, Muzikansky, Ariela, Morse, Richard, Fitzgerald, Laura, Morin, Haley D., Brower, Roy G., Reineck, Lora A., Bienstock, Karen, Steingrub, Jay H., Hou, Peter K., Steingrub, Jay S., Tidswell, Mark A., Kozikowski, Lori-Ann, Kardos, Cynthia, DeSouza, Leslie, Romain, Sarah, Thornton-Thompson, Sherell, Talmor, Daniel, Shapiro, Nathan, Andromidas, Konstantinos, Banner-Goodspeed, Valerie, Bolstad, Michael, Boyle, Katherine L., Cabrera, Payton, deVilla, Arnaldo, Ellis, Joshua C., Grafals, Ana, Hayes, Sharon, Higgins, Conor, Kurt, Lisa, Kurtzman, Nicholas, Redman, Kimberly, Rosseto, Elinita, Scaffidi, Douglas, Filbin, Michael R., Hibbert, Kathryn A., Parry, Blair, Margolin, Justin, Hillis, Brooklynn, Hamer, Rhonda, Brait, Kelsey, Beakes, Caroline, McKaig, Brenna, Kugener, Eleonore, Jones, Alan E., Galbraith, James, Nandi, Utsav, Peacock, Rebekah, Hendey, Gregory, Kangelaris, Kirsten, Ashktorab, Kimia, Gropper, Rachel, Agrawal, Anika, Yee, Kimberley J., Jauregui, Alejandra E., Zhuo, Hanjing, Almasri, Eyad, Fayed, Mohamed, Hubel, Kinsley A., Hughes, Alyssa R., Garcia, Rebekah L., Lim, George W., Chang, Steven Y., Lin, Michael Y., Vargas, Julia, Sihota, Hena, Beutler, Rebecca, Agarwal, Trisha, Wilson, Jennifer G., Vojnik, Rosemary, Perez, Cynthia, McDowell, Jordan H., Roque, Jonasel, Wang, Henry, Huebinger, Ryan M., Patel, Bela, Vidales, Elizabeth, Albertson, Timothy, Hardy, Erin, Harper, Richart, Moss, Marc A., Baduashvili, Amiran, Chauhan, Lakshmi, Douin, David J., Martinez, Flora, Finck, Lani L., Bastman, Jill, Howell, Michelle, Higgins, Carrie, McKeehan, Jeffrey, Finigan, Jay, Stubenrauch, Peter, Janssen, William J., Griesmer, Christine, VerBurg, Olivia, Hyzy, Robert C., Park, Pauline K., Nelson, Kristine, McSparron, Jake I., Co, Ivan N., Wang, Bonnie R., Jimenez, Jose, Olbrich, Norman, McDonough, Kelli, Jia, Shijing, Hanna, Sinan, Gong, Michelle N., Richardson, Lynne D., Nair, Rahul, Lopez, Brenda, Amosu, Omowunmi, Offor, Obiageli, Tzehaie, Hiwet, Nkemdirim, William, Boujid, Sabah, Mosier, Jarrod M., Hypes, Cameron, Campbell, Elizabeth Salvagio, Bixby, Billie, Gilson, Boris, Lopez, Anitza, Bime, Christian, Parthasarathy, Sairam, Cano, Ariana M., Hite, R. Duncan, Terndrup, Thomas E., Wiedemann, Herbert P., Hudock, Kristin, Tanzeem, Hammad, More, Harshada, Martinkovic, Jamie, Sellers, Susan, Houston, Judy, Burns, Mary, Kiran, Simra, Roads, Tammy, Kennedy, Sarah, Duggal, Abhijit, Thiruchelvam, Nirosshan, Ashok, Kiran, King, Alexander H., Mehkri, Omar, Dugar, Siddharth, Sahoo, Debasis, Yealy, Donald M., Angus, Derek C., Weissman, Alexandra J., Vita, Tina M., Berryman, Emily, Hough, Catherine L., Khan, Akram, Krol, Olivia F., Mills, Emmanuel, Kinjal, Mistry, Briceno, Genesis, Reddy, Raju, Hubel, Kinsley, Jouzestani, Milad K., McDougal, Madeline, Deshmukh, Rupali, Johnston, Nicholas J., Robinson, Bryce H., Gundel, Staphanie J., Katsandres, Sarah C., Chen, Peter, Torbati, Sam S., Parimon, Tanyalak, Caudill, Antonina, Mattison, Brittany, Jackman, Susan E., Chen, Po-En, Bayoumi, Emad, Ojukwu, Cristabelle, Fine, Devin, Weissberg, Gwendolyn, Isip, Katherine, Choi-Kuaea, Yunhee, Mehdikhani, Shaunt, Dar, Tahir B., Fleury Augustin, Nsole Biteghe, Tran, Dana, Dukov, Jennifer Emilow, Matusov, Yuri, Choe, June, Hindoyan, Niree A., Wynter, Timothy, Pascual, Ethan, Clapham, Gregg J., Herrera, Lisa, Caudill, Antonia, O’Mahony, D. Shane, Nyatsatsang, Sonam T., Wilson, David M., Wallick, Julie A., Duven, Alexandria M., Fletcher, Dakota D., Miller, Chadwick, Files, D. Clark, Gibbs, Kevin W., Flores, Lori S., LaRose, Mary E., Landreth, Leigha D., Palacios, D. Rafael, Parks, Lisa, Hicks, Madeline, Goodwin, Andrew J., Kilb, Edward F., Lematty, Caitlan T., Patti, Kerilyn, Grady, Abigail, Rasberry, April, Morris, Peter E., Sturgill, Jamie L., Cassity, Evan P., Dhar, Sanjay, Montgomery-Yates, Ashley A., Pasha, Sarah N., Mayer, Kirby P., Pharm.D., Brittany Bissel, Trott, Terren, Rehman, Shahnaz, de Wit, Marjolein, Mason, Jessica, Bledsoe, Joseph, Knowlton, Kirk U., Brown, Samuel, Lanspa, Michael, Leither, Lindsey, Pelton, Ithan, Armbruster, Brent P., Montgomery, Quinn, Kumar, Naresh, Fergus, Melissa, Imel, Karah, Palmer, Ghazal, Webb, Brandon, Klippel, Carolyn, Jensen, Hannah, Duckworth, Sarah, Gray, Andrew, Burke, Tyler, Knox, Dan, Lumpkin, Jenna, Aston, Valerie T., Applegate, Darrin, Serezlic, Erna, Brown, Katie, Merril, Mardee, Harris, Estelle S., Middleton, Elizabeth A., Barrios, Macy A.G., Greer, Jorden, Schmidt, Amber D., Webb, Melissa K., Paine, Roert, Callahan, Sean J., Waddoups, Lindsey J., Yamane, Misty B., Self, Wesley H., Rice, Todd W., Casey, Jonathan D., Johnson, Jakea, Gray, Christopher, Hays, Margaret, Roth, Megan, Menon, Vidya, Kasubhai, Moiz, Pillai, Anjana, Daniel, Jean, Sittler, Daniel, Kanna, Balavenkatesh, Jilani, Nargis, Amaro, Francisco, Santana, Jessica, Lyakovestsky, Aleksandr, Madhoun, Issa, Desroches, Louis Marie, Amadon, Nicole, Bahr, Alaa, Ezzat, Imaan, Guerrero, Maryanne, Padilla, Joane, Fullmer, Jessie, Singh, Inderpreet, Ali Shah, Syed Hamad, Narang, Rajeev, Mock, Polly, Shadle, Melissa, Hernandez, Brenda, Welch, Kevin, Payne, Andrea, Ertl, Gabriela, Canario, Daniel, Barrientos, Isabel, Goss, Danielle, DeVries, Mattie, Folowosele, Ibidolapo, Garner, Dorothy, Gomez, Mariana, Price, Justin, Bansal, Ekta, Wong, Jim, Faulhaber, Jason, Fazili, Tasaduq, Yeary, Brian, Ndolo, Ruth, Bryant, Christina, Smigeil, Bridgette, Robinson, Philip, Najjar, Rana, Jones, Patrice, Nguyen, Julie, Chin, Christina, Taha, Hassan, Najm, Salah, Smith, Christopher, Moore, Jason, Nassar, Talal, Gallinger, Nick, Christian, Amy, Mauer, D’Amber, Phipps, Ashley, Waters, Michael, Zepeda, Karla, Coslet, Jordan, Landazuri, Rosalynn, Pineda, Jacob, Uribe, Nicole, Garcia, Jose Ruiz, Barbabosa, Cecilia, Sandler, Kaitlyn, Overcash, J. Scott, Marquez, Adrienna, Chu, Hanh, Lee, Kia, Quillin, Kimberly, Garcia, Andrea, Lew, Pauline, Rogers, Ralph, Shehadeh, Fadi, Mylona, Evangelia K., Kaczynski, Matthew, Tran, Quynh-Lam, Benitez, Gregorio, Mishra, Biswajit, Felix, Lewis Oscar, Vafea, Maria Tsikala, Atalla, Eleftheria, Davies, Robin, Hedili, Salma, Monkeberg, Maria Andrea, Tabler, Sandra, Harrington, Britt, Meegada, Sreenath, Koripalli, Venkata Sandeep, Muddana, Prithvi, Jain, Lakshay, Undavalli, Chaitanya, Kavya, Parasa, Ibiwoye, Mofoluwaso, Akilo, Hameed, Lovette, Bryce D., Wylie, Jamie-Crystal, Smith, Diana M., Poon, Kenneth, Eckardt, Paula, Heysu, Rubio-Gomez, Sundararaman, Nithya, Alaby, Doris, Sareli, Candice, Sánchez, Adriana, Popielski, Laura, Kambo, Amy, Viens, Kimberley, Turner, Melissa, Vjecha, Michael J., Weintrob, Amy, Brar, Indira, Markowitz, Norman, Pastor, Erika, Corpuz, Roweena, Alangaden, George, McKinnon, John, Ramesh, Mayur, Herc, Erica, Yared, Nicholas, Lanfranco, Odaliz Abreu, Rivers, Emanuel, Swiderek, Jennifer, Gupta, Ariella Hodari, Pabla, Pardeep, Eliya, Sonia, Jazrawi, Jehan, Delor, Jeremy, Desai, Mona, Cook, Aaron, Jaehne, Anja Kathrina, Gill, Jasreen Kaur, Renaud, Sheri, Sarveswaran, Siva, Gardner, Edward, Scott, James, Bianchini, Monica, Melvin, Casey, Kim, Gina, Wyles, David, Kamis, Kevin, Miller, Rachel, Douglas, Ivor, Haukoos, Jason, Hicks, Carrie, Lazarte, Susana, Marines-Price, Rubria, Osuji, Alice, Agbor Agbor, Barbine Tchamba, Petersen, Tianna, Kamel, Dena, Hansen, Laura, Garcia, Angie, Cha, Christine, Mozaffari, Azadeh, Hernandez, Rosa, Cutrell, James, Kim, Mina, DellaValle, Natalie, Gonzales, Sonia, Somboonwit, Charurut, Oxner, Asa, Guerra, Lucy, Hayes, Michael, Nguyen, Thi, Tran, Thanh, Pinto, Avenette, Hatlen, Timothy, Anderson, Betty, Zepeda-Gutierrez, Ana, Martin, Dannae, Temblador, Cindi, Cuenca, Avon, Tanoviceanu, Roxanne, Prieto, Martha, Guerrero, Mario, Daar, Eric, Correa, Ramiro, Hartnell, Gabe, Wortmann, Glenn, Doshi, Saumil, Moriarty, Theresa, Gonzales, Melissa, Garman, Kristin, Baker, Jason V., Frosch, Anne, Goldsmith, Rachael, Driver, Brian, Frank, Christine, Leviton, Tzivia, Prekker, Matthew, Jibrell, Hodan, Lo, Melanie, Klaphake, Jonathan, Mackedanz, Shari, Ngo, Linh, Garcia-Myers, Kelly, Kunisaki, Ken M., Wendt, Chris, Melzer, Anne, Wetherbee, Erin, Drekonja, Dimitri, Pragman, Alexa, Hamel, Aimee, Thielen, Abbie, Hassler, Miranda, Walquist, Mary, Augenbraun, Michael, George, Jensen, Demeo, Lynette, Mishko, Motria, Thomas, Lorraine, Tatem, Luis, Dehovitz, Jack, Abassi, Mahsa, Leuck, Anne-Marie, Rao, Via, Pullen, Matthew, Luke, Darlette, LaBar, Derek, Christiansen, Theresa, Howard, Diondra, Biswas, Kousick, Harrington, Cristin, Garcia, Amanda, Bremer, Tammy, Burke, Tara, Koker, Brittany, Davis-Karim, Anne, Pittman, David, Vasudeva, Shikha S., Johnstone, Jaylynn R., Agnetti, Kate, Davis, Ruby, Trautner, Barbara, Hines-Munson, Casey, Van, John, Dillon, Laura, Wang, Yiqun, Nagy-Agren, Stephanie, Vasudeva, Shikha, Ochalek, Tracy, Caldwell, Erin, Humerickhouse, Edward, Boone, David, McGraw, William, Looney, David J., Mehta, Sanjay R., Johns, Scott Thompson, St. John, Melissa, Raceles, Jacqueline, Sear, Emily, Funk, Stephen, Cesarini, Rosa, Fang, Michelle, Nicalo, Keith, Drake, Wonder, Jones, Beatrice, Holtman, Teresa, Nguyen, Hien H., Maniar, Archana, Johnson, Eric A., Nguyen, Lam, Tran, Michelle T., Barrett, Thomas W., Johnston, Tera, Huggins, John T., Beiko, Tatsiana Y., Hughes, Heather Y., McManigle, William C., Tanner, Nichole T., Washburn, Ronald G., Ardelt, Magdalena, Tuohy, Patricia A., Mixson, Jennifer L., Hinton, Charles G., Thornley, Nicola, Allen, Heather, Elam, Shannon, Boatman, Barry, Baber, Brittany J., Ryant, Rudell, Roller, Brentin, Nguyen, Chinh, Mikail, Amani Morgan, Research, Marivic Hansen, Lichtenberger, Paola, Baracco, Gio, Ramos, Carol, Bjork, Lauren, Sueiro, Melyssa, Tien, Phyllis, Freasier, Heather, Buck, Theresa, Nekach, Hafida, Holodniy, Mark, Chary, Aarthi, Lu, Kan, Peters, Theresa, Lopez, Jessica, Tan, Susanna Yu, Lee, Robert H., Asghar, Aliya, Karyn Isip, Tasadduq Karim, Le, Katherine, Nguyen, Thao, Wong, Shinn, Raben, Dorthe, Murray, Daniel D., Jensen, Tomas O., Peters, Lars, Aagaard, Bitten, Nielsen, Charlotte B., Krapp, Katharina, Nykjær, Bente Rosdahl, Olsson, Christina, Kanne, Katja Lisa, Grevsen, Anne Louise, Joensen, Zillah Maria, Bruun, Tina, Bojesen, Ane, Woldbye, Frederik, Normand, Nick E., Esman, Frederik V.L., Benfield, Thomas, Clausen, Clara Lundetoft, Hovmand, Nichlas, Israelsen, Simone Bastrup, Iversen, Katrine, Leding, Caecilie, Pedersen, Karen Brorup, Thorlacius-Ussing, Louise, Tinggaard, Michaela, Tingsgard, Sandra, Krohn-Dehli, Louise, Pedersen, Dorthe, Villadsen, Signe, Staehr Jensen, Jens-Ulrik, Overgaard, Rikke, Rastoder, Ema, Heerfordt, Christian, Hedsund, Caroline, Ronn, Christian Phillip, Kamstrup, Peter Thobias, Hogsberg, Dorthe Sandbaek, Bergsoe, Christina, Søborg, Christian, Hissabu, Nuria M.S., Arp, Bodil C., Ostergaard, Lars, Staerke, Nina Breinholt, Yehdego, Yordanos, Sondergaard, Ane, Johansen, Isik S., Pedersen, Andreas Arnholdt, Knudtzen, Fredrikke C., Larsen, Lykke, Hertz, Mathias A., Fabricius, Thilde, Holden, Inge K., Lindvig, Susan O., Helleberg, Marie, Gerstoft, Jan, Kirk, Ole, Jensen, Tomas Ostergaard, Madsen, Birgitte Lindegaard, Pedersen, Thomas Ingemann, Harboe, Zitta Barrella, Roge, Birgit Thorup, Hansen, Thomas Michael, Glesner, Matilde Kanstrup, Lofberg, Sandra Valborg, Nielsen, Ariella Denize, Leicht von Huth, Sebastian, Nielsen, Henrik, Thisted, Rikke Krog, Petersen, Kristine Toft, Juhl, Maria Ruwald, Podlekareva, Daria, Johnsen, Stine, Andreassen, Helle Frost, Pedersen, Lars, Clara Ellinor Lindnér, Cecilia Ebba, Wiese, Lothar, Knudsen, Lene Surland, Skrøder Nytofte, Nikolaj Julian, Havmøller, Signe Ravn, Expósito, Maria, Badillo, José, Martínez, Ana, Abad, Elena, Chamorro, Ana, Figuerola, Ariadna, Mateu, Lourdes, España, Sergio, Lucero, Maria Constanza, Santos, José Ramón, Lladós, Gemma, Lopez, Cristina, Carabias, Lydia, Molina-Morant, Daniel, Loste, Cora, Bracke, Carmen, Siles, Adrian, Fernández-Cruz, Eduardo, Di Natale, Marisa, Padure, Sergiu, Gomez, Jimena, Ausin, Cristina, Cervilla, Eva, Balastegui, Héctor, Sainz, Carmen Rodríguez, Lopez, Paco, Carbone, Javier, Escobar, Mariam, Balerdi, Leire, Legarda, Almudena, Roldan, Montserrat, Letona, Laura, Muñoz, José, Camprubí, Daniel, Arribas, Jose R., Sánchez, Rocio Montejano, Díaz-Pollán, Beatriz, Stewart, Stefan Mark, Garcia, Irene, Borobia, Alberto, Mora-Rillo, Marta, Estrada, Vicente, Cabello, Noemi, Nuñez-Orantos, M.J., Sagastagoitia, I., Homen, J.R., Orviz, E., Montalvá, Adrián Sánchez, Espinosa-Pereiro, Juan, Bosch-Nicolau, Pau, Salvador, Fernando, Burgos, Joaquin, Morales-Rull, Jose Luis, Moreno Pena, Anna Maria, Acosta, Cristina, Solé-Felip, Cristina, Horcajada, Juan P., Sendra, Elena, Castañeda, Silvia, López-Montesinos, Inmaculada, Gómez-Junyent, Joan, Gonzáles, Carlota Gudiol, Cuervo, Guilermo, Pujol, Miquel, Carratalà, Jordi, Videla, Sebastià, Günthard, Huldrych, Braun, Dominique L., West, Emily, M’Rabeth-Bensalah, Khadija, Eichinger, Mareile L., Grüttner-Durmaz, Manuela, Grube, Christina, Zink, Veronika, pharmacist, Goes pharmacist, Josefine, Fätkenheuer, Gerd, Malin, Jakob J., Tsertsvadze, Tengiz, Abutidze, Akaki, Chkhartishvili, Nikoloz, Metchurtchlishvili, Revaz, Endeladze, Marina, Paciorek, Marcin, Bursa, Dominik, Krogulec, Dominika, Pulik, Piotr, Ignatowska, Anna, Horban, Andrzej, Bakowska, Elzbieta, Kowaska, Justyna, Bednarska, Agnieszka, Jurek, Natalia, Skrzat-Klapaczynska, Agata, Bienkowski, Carlo, Hackiewicz, Malgorzata, Makowiecki, Michal, Platowski, Antoni, Fishchuk, Roman, Kobrynska, Olena, Levandovska, Khrystyna, Kirieieva, Ivanna, Kuziuk, Mykhailo, Naucler, Pontus, Perlhamre, Emma, Mazouch, Lotta, Kelleher, Anthony, Polizzotto, Mark, Carey, Catherine, Chang, Christina C., Hough, Sally, Virachit, Sophie, Davidson, Sarah, Bice, Daniel J., Ognenovska, Katherine, Cabrera, Gesalit, Flynn, Ruth, Young, Barnaby E., Chia, Po Ying, Lee, Tau Hong, Lin, Ray J., Lye, David C., Ong, Sean W.X., Puah, Ser Hon, Yeo, Tsin Wen, Diong, Shiau Hui, Ongko, Juwinda, Yeo, He Ping, Eriobu, Nnakelu, Kwaghe, Vivian, Zaiyad, Habib, Idoko, Godwin, Uche, Blessing, Selvamuthu, Poongulali, Kumarasamy, Nagalingeswaran, Beulah, Faith Ester, Govindarajan, Narayan, Mariyappan, Kowsalya, Losso, Marcelo H., Abela, Cecilia, Moretto, Renzo, Belloc, Carlos G., Ludueña, Jael, Amar, Josefina, Toibaro, Javier, Macias, Laura Moreno, Fernandez, Lucia, Frare, Pablo S., Chaio, Sebastian R., Pachioli, Valeria, Timpano, Stella M., Sanchez, Marisa del Lujan, de Paz Sierra, Mariana, Stanek, Vanina, Belloso, Waldo, Cilenti, Flavia L., Valentini, Ricardo N., Stryjewski, Martin E., Locatelli, Nicolas, Soler Riera, Maria C., Salgado, Clara, Baeck, Ines M., Di Castelnuovo, Valentina, Zarza, Stella M., Hudson, Fleur, Parmar, Mahesh K.B., Goodman, Anna L., Dphil, Badrock, Jonathan, Gregory, Adam, Goodall, Katharine, Harris, Nicola, Wyncoll, James, Bhagani, S., Rodger, A., Luntiel, A., Patterson, C., Morales, J., Witele, E., Preston, A.-M., Nandani, A., Price, D.A., Hanrath, Aiden, Nell, Jeremy, Patel, Bijal, Hays, Carole, Jones, Geraldine, Davidson, Jade, Bawa, T., Mathews, M., Mazzella, A., Bisnauthsing, K., Aguilar-Jimenez, L., Borchini, F., Hammett, S., Touloumi, Giota, Pantazis, Nikos, Gioukari, Vicky, Souliou, Tania, Antoniadou, A., Protopapas, K., Kavatha, D., Grigoropoulou, S., Oikonomopoulo, C., Moschopoulos, C., Koulouris, N.G., Tzimopoulos, K., Koromilias, A., Argyraki, K., Lourida, P., Bakakos, P., Kalomenidis, I., Vlachakos, V., Barmparessou, Z., Balis, E., Zakynthinos, S., Sigala, I., Gianniou, N., Dima, E., Magkouta, S., Synolaki, E., Konstanta, S., Vlachou, M., Stathopoulou, P., Panagopoulos, P., Petrakis, V., Papazoglou, D., Tompaidou, E., Isaakidou, E., Poulakou, G., Rapti, V., Leontis, K., Nitsotolis, T., Athanasiou, K., Syrigos, K., Kyriakoulis, K., Trontzas, I., Arfara-Melanini, M., Kolonis, V., Kityo, Cissy, Mugerwa, Henry, Kiweewa, Francis, Kimuli, Ivan, Lukaakome, Joseph, Nsereko, Christoher, Lubega, Gloria, Kibirige, Moses, Nakahima, William, Wangi, Deus, Aguti, Evelyne, Generous, Lilian, Massa, Rosemary, Nalaki, Margaret, Magala, Felix, Nabaggala, Phiona Kaweesi, Kidega, Robert, Faith, Oryem Daizy, Florence, Apio, Emmanuel, Ocung, Beacham, Mugoonyi Paul, Geoffrey, Amone, Nakiboneka, Dridah, Apiyo, Paska, Kirenga, Bruce, Atukunda, Angella, Muttamba, Winters, Remmy, Kyeyume, Segawa, Ivan, Pheona, Nsubuga, Kigere, David, Mbabazi, Queen Lailah, Boersalino, Ledra, Nyakoolo, Grace, Fred, Aniongo, Alupo, Alice, Ebong, Doryn, Monday, Edson, Nalubwama, Ritah Norah, Kainja, Milton, Ambrose, Munu, Kwehayo, Vanon, Nalubega, Mary Grace, Ongoli, Augustine, Obbo, Stephen, Sebudde, Nicholus, Alaba, Jeniffer, Magombe, Geoffrey, Tino, Harriet, Obonya, Emmanuel, Lutaakome, Joseph, Kitonsa, Jonathan, Onyango, Martin, Naboth, Tukamwesiga, Naluyinda, Hadijah, Nanyunja, Regina, Irene, Muttiibwa, Jane, Biira, Wimfred, Kyobejja, Leonard, Ssemazzi, Deus, Tkiinomuhisha, Babra, Namasaba, Taire, Paul, Nabankema, Evelyn, Ogavu, Joseph, Mugerwa, Oscar, Okoth, Ivan, Mwebaze, Raymond, Mugabi, Timothy, Makhoba, Anthony, Arikiriza, Phiona, Theresa, Nabuuma, Nakayima, Hope, Frank, Kisuule, Ramgi, Patrícia, Pereira, Kássia, Osinusi, Anu, Cao, Huyen, Klekotka, Paul, Price, Karen, Nirula, Ajay, Osei, Suzette, Tipple, Craig, Wills, Angela, Peppercorn, Amanda, Watson, Helen, Gupta, Rajesh, Alexander, Elizabeth, Mogalian, Erik, Lin, Leo, Ding, Xiao, Margolis, David, Yan, Li, Girardet, Jean-Luc, Ma, Ji, Hong, Zhi, Zhu, Quing, Seegobin, Seth, Gibbs, Michael, Latchman, Mickel, Hasior, Katarzyna, Bouquet, Jerome, Wei, Jianxin, Streicher, Katie, Schmelzer, Albert, Brooks, Dennis, Butcher, Jonny, Tonev, Dimitar, Arbetter, Douglas, Damstetter, Philippe, Legenne, Philippe, Stumpp, Michael, Goncalves, Susana, Ramanathan, Krishnan, Chandra, Richa, Baseler, Beth, Teitelbaum, Marc, Schechner, Adam, Holley, H. Preston, Jankelevich, Shirley, Becker, Nancy, Dolney, Suzanne, Hissey, Debbie, Simpson, Shelly, Kim, Mi Ha, Beeler, Joy, Harmon, Liam, Asomah, Mabel, Jato, Yvonne, Stottlemyer, April, Tang, Olivia, Vanderpuye, Sharon, Yeon, Lindsey, Buehn, Molly, Eccard-Koons, Vanessa, Frary, Sadie, MacDonald, Leah, Cash, Jennifer, Hoopengardner, Lisa, Linton, Jessica, Schaffhauser, Marylu, Nelson, Michaela, Spinelli-Nadzam, Mary, Proffitt, Calvin, Lee, Christopher, Engel, Theresa, Fontaine, Laura, Osborne, C.K., Hohn, Matt, Galcik, Michael, Thompson, DeeDee, Kopka, Stacey, Shelley, Denise M., Mendez, Gregg, Brown, Shawn, Albert, Sara, Balde, Abby, Baracz, Michelle, Bielica, Mona, Billouin-Frazier, Shere, Choudary, Jay, Dixon, Mary, Eyler, Carolyn, Frye, Leanne, Gertz, Jensen, Giebeig, Lisa, Gulati, Neelam, Hankinson, Liz, Hogarty, Debi, Huber, Lynda, Krauss, Gary, Lake, Eileen, Manandhar, Meryan, Rudzinski, Erin, Sandrus, Jen, Suders, Connie, Natarajan, Ven, Rupert, Adam W., Baseler, Michael, Lynam, Danielle, Imamichi, Tom, Laverdure, Sylvain, McCormack, Ashley, Paudel, Sharada, Cook, Kyndal, Haupt, Kendra, Khan, Ayub, Hazen, Allison, Badralmaa, Yunden, Smith, Kenneth, Patel, Bhakti, Kubernac, Amanda, Kubernac, Robert, Hoover, Marie L., Solomon, Courtney, Rashid, Marium, Murphy, Joseph, Brown, Craig, DuChateau, Nadine, Ellis, Sadie, Flosi, Adam, Fox, Lisa, Johnson, Les, Nelson, Rich, Stojanovic, Jelena, Treagus, Amy, Wenner, Christine, Williams, Richard, Jensen, Tomas O, Murray, Thomas A, Grandits, Greg A, Jain, Mamta K, Shaw-Saliba, Kathryn, Matthay, Michael A, Baker, Jason V, Dewar, Robin L, Goodman, Anna L, Hatlen, Timothy J, Highbarger, Helene C, Lallemand, Perrine, Leshnower, Bradley G, Looney, David, Moschopoulos, Charalampos D, Murray, Daniel D, Mylonakis, Eleftherios, Rehman, M Tauseef, Rupert, Adam, Stevens, Randy, Turville, Stuart, Wick, Katherine, Lundgren, Jens, and Ko, Emily R

Published
2024
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33. AASAE: Augmentation-Augmented Stochastic Autoencoders

Author

Falcon, William, Jha, Ananya Harsh, Koker, Teddy, and Cho, Kyunghyun

Subjects
Computer Science - Machine Learning
Abstract

Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches. Their success can largely be attributed to data augmentation pipelines which generate multiple views of a single input that preserve the underlying semantics. In this work, we introduce augmentation-augmented stochastic autoencoders (AASAE), yet another alternative to self-supervised learning, based on autoencoding. We derive AASAE starting from the conventional variational autoencoder (VAE), by replacing the KL divergence regularization, which is agnostic to the input domain, with data augmentations that explicitly encourage the internal representations to encode domain-specific invariances and equivariances. We empirically evaluate the proposed AASAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated. Our experiments confirm the effectiveness of data augmentation as a replacement for KL divergence regularization. The AASAE outperforms the VAE by 30% on CIFAR-10, 40% on STL-10 and 45% on Imagenet. On CIFAR-10 and STL-10, the results for AASAE are largely comparable to the state-of-the-art algorithms for self-supervised learning., Comment: 17 pages, 5 figures, 3 table

Published
2021

35. Decreasing central line-associated bloodstream infections rates in intensive care units in 30 low- and middle-income countries: An INICC approach

Author

Rosenthal, Victor D., Jin, Zhilin, Brown, Eric C., Dongol, Reshma, De Moros, Daisy A., Alarcon-Rua, Johana, Perez, Valentina, Stagnaro, Juan P., Alkhawaja, Safaa, Jimenez-Alvarez, Luisa F., Cano-Medina, Yuliana A., Valderrama-Beltran, Sandra L., Henao-Rodas, Claudia M., Zuniga-Chavarria, Maria A., El-Kholy, Amani, Agha, Hala, Sahu, Suneeta, Mishra, Shakti B., Bhattacharyya, Mahuya, Kharbanda, Mohit, Poojary, Aruna, Nair, Pravin K., Myatra, Sheila N., Chawla, Rajesh, Sandhu, Kavita, Mehta, Yatin, Rajhans, Prasad, Abdellatif-Daboor, Mohammad, Chian-Wern, Tai, Gan, Chin Seng, Mohd-Basri, Mat Nor, Aguirre-Avalos, Guadalupe, Hernandez-Chena, Blanca E., Sassoe-Gonzalez, Alejandro, Villegas-Mota, Isabel, Aleman- Bocanegra, Mary C., Bat-Erdene, Ider, Carreazo, Nilton Y., Castaneda-Sabogal, Alex, Janc, Jarosław, Hlinkova, Sona, Yildizdas, Dincer, Havan, Merve, Koker, Alper, Sungurtekin, Hulya, Dinleyici, Ener C., Guclu, Ertugrul, Tao, Lili, Memish, Ziad A., and Yin, Ruijie

Published
2024
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37. U-Noise: Learnable Noise Masks for Interpretable Image Segmentation

Author

Koker, Teddy, Mireshghallah, Fatemehsadat, Titcombe, Tom, and Kaissis, Georgios

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep Neural Networks (DNNs) are widely used for decision making in a myriad of critical applications, ranging from medical to societal and even judicial. Given the importance of these decisions, it is crucial for us to be able to interpret these models. We introduce a new method for interpreting image segmentation models by learning regions of images in which noise can be applied without hindering downstream model performance. We apply this method to segmentation of the pancreas in CT scans, and qualitatively compare the quality of the method to existing explainability techniques, such as Grad-CAM and occlusion sensitivity. Additionally we show that, unlike other methods, our interpretability model can be quantitatively evaluated based on the downstream performance over obscured images., Comment: ICIP 2021. Revision: corrected affiliation and reference

Published
2021
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43. Applying Machine Learning Techniques to Forecast Demand in a South African Fast-Moving Consumer Goods Company

Author

Chanza, Martin, De Koker, Louise, Boucher, Sasha, Munapo, Elias, Mabuza, Gugulethu, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Shamsul Arefin, Mohammad, editor, Panchenko, Vladimir, editor, Thomas, J. Joshua, editor, Munapo, Elias, editor, Weber, Gerhard-Wilhelm, editor, and Rodriguez-Aguilar, Roman, editor

Published
2023
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44. Re-assessing the Role of the Statistician in the Era of Big Data: A Business Perspective

Author

De Koker, Louise, Tati, Gabriel, Chanza, Martin, Munapo, Elias, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Vasant, Pandian, editor, Weber, Gerhard-Wilhelm, editor, Marmolejo-Saucedo, José Antonio, editor, Munapo, Elias, editor, and Thomas, J. Joshua, editor

Published
2023
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45. On information value and decision analyses

Author

Thöns, Sebastian, Caprani, Colin, Faber, Michael Havbro, Frangopol, Dan M., Gardoni, Paolo, Giordano, Pier Francesco, Honfi, Daniel, Iannacone, Leandro, Khan, Mohammad Shihabuddin, Köhler, Jochen, Kim, Sunyong, de Koker, Nico, Limongelli, Maria Pina, Miraglia, Simona, Nielsen, Jannie Sønderkær, Pandey, Mahesh, and Viljoen, Celeste

Published
2024
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